Detecting and investigating complex fraud schemes may require a deeper understanding of the relationship between or among actors, events and entities than is currently available. Methods and algorithms exist for analyzing social networks in order to determine marketing strategies to deploy within social networks, such as the Facebook network, the LinkedIn network, or in a network of e-mail contacts, for example. These methods may identify responsive target customers for new product launches that then proliferate through the network of these target customers.
Financial fraud schemes may operate through a network of entities and links between entities. Methods used for identifying effective target customers may be used to identify entities and surround networks that are at risk for suspicious financial activity. Within financial networks with a large amount of entities (e.g., customers, agents, suppliers, merchants, financial institutions, bank accounts, and more), it may not be possible to efficiently examine the entire network for suspicious activity. While predictive algorithms exist that may be able to provide a risk score for a financial network, the algorithms may be difficult to apply to large financial networks. Identifying entities and their networks that are at high risk for a financial fraud event may prevent financial fraud from occurring or allow authorities to monitor the network before the event occurs.